Dive into our latest publications and stay updated on our advancements.
JANUARY,2025
Autonomous learning of generative models with chemical reaction network ensembles
Can a micron-sized sack of interacting molecules autonomously learn an internal model of a complex and fluctuating environment?
FEBRUARY,2022
“Life Is Matter Plus Algorithms”
Molecular testing will play an increasingly important role, and with the help of algorithms will enter a broadband era of higher, speed, bandwidth, and accuracy of testing.
APRIL,2021
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
Tapestry combines ideas from compressed sensing and combinatorial group testing to create a new kind of algorithm that is very effective in deconvoluting pooled tests.
MARCH,2021
What Happens To All Those COVID Test Nasal Swabs? That’s Where It Gets Complicated.
Tapestry Pooling has developed an algorithm that would allow several tests to be screened at the same time.
OCTOBER,2020
Large-scale Testing for SARS-CoV-2 using Tapestry Pooling
Here we report on validation studies with Covid19 patient samples for the Tapestry Pooling scheme with prevalence in the range of 1% to 2%.
OCTOBER,2020
AMERICAN MATHEMATICAL SOCIETY
While the COVID-19 pandemic has brought tragedy and disruption, it has also provided a unique opportunity for mathematics to play an important and visible role in addressing a pressing issue facing our society.
AUGUST,2020
The Mathematics of Mass Testing for COVID-19
Statistician David Donoho reviewed a recently emergent research front in which mathematical and statistical ideas which can enable a rapid expansion of testing capabilities worldwide.
JULY,2020
A Compressed Sensing Approach to Group-Testing for COVID-19 Detection
Indian Institute of Technology Bombay (IIT-B)-led a multi-institute the research team has developed a new testing protocol that can detect asymptomatic patients infected with the coronavirus.
MAY,2020
Tapestry: A Single-Round Smart Pooling Technique for COVID-19 Testing
We present Tapestry, a novel quantitative nonadaptive pooling scheme to test many samples using only a few tests.
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